SoroushVahidi/scidocs huggingface dataset:
A Comparison of Sender-Initiated and Receiver-Initiated Reliable Multicast Protocols
(1994)

Sender-initiated reliable multicast protocols, based on the use of positive acknowledgments (ACKs), lead to an ACK implosion problem at the sender as the number of receivers increases. Briefly, the ACK implosion problem refers to the significant overhead incurred by the sending host due to the processing of ACKs from each receiver. A potential solution to this problem is to shift the burden of providing reliable data transfer to the receivers—thus resulting in a receiver-initiated multicast error control protocol based on the use of negative acknowledgments (NAKs). In this paper we determine the maximum throughputs of the sending and receiving hosts for generic sender-initiated and receiver-initiated protocols. We show that the receiver-initiated error control protocols provide substantially higher throughputs than their sender-initiated counterparts. We further demonstrate that the introduction of random delays prior to generating NAKs coupled with the multicasting of NAKs to all receivers has the potential for an additional substantial increase in the throughput of receiver-initiated error control protocols over sender-initiated protocols.
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
(2015)

In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
Bridging Neural Machine Translation and Bilingual Dictionaries
(2016)

Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.
Approximate image-based tree-modeling using particle flows
(2007)

We present a method for producing 3D tree models from input photographs with only limited user intervention. An approximate voxel-based tree volume is estimated using image information. The density values of the voxels are used to produce initial positions for a set of particles. Performing a 3D flow simulation, the particles are traced downwards to the tree basis and are combined to form twigs and branches. If possible, the trunk and the first-order branches are determined in the input photographs and are used as attractors for particle simulation. The geometry of the tree skeleton is produced using botanical rules for branch thicknesses and branching angles. Finally, leaves are added. Different initial seeds for particle simulation lead to a variety, yet similar-looking branching structures for a single set of photographs.
The Geometry of Algorithms with Orthogonality Constraints
(1998)

In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal processing. In addition to the new algorithms, we show how the geometrical framework gives penetrating new insights allowing us to create, understand, and compare algorithms. The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms. It is our hope that developers of new algorithms and perturbation theories will benefit from the theory, methods, and examples in this paper.
Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree
(2015)

This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.
Imbalanced Protein Data Classification Using Ensemble FTM-SVM
(2015)

Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods.
A Software Gamification Model for Cross-Cultural Software Development Teams
(2017)

Gamification is the use of game design elements in non-game context platforms such as services and marketing to motivate people to participate in planned activities to increase engagement and loyalty to achieve goals.
 Gamification has been applied to academic fields including software engineering in recent years. Some studies show that gamification can motivate engineers in Software Engineering (SE) if applied appropriately. However, most gamification implementations are lacking well-defined frameworks or models.
 This paper develops a software gamification model (SGM) with a well-defined framework that provides a robust process for implementing gamification for SE. This model also contains elements from applied psychology, Social Software Engineering (SSE), and the Capability Maturity Model (CMM). Having those elements ensures that the model has a solid foundation in gamification and SE disciplines. The model is further tailored for cross-cultural software development teams (CCSDT).
 CCSDT have been popular in recent years due to the fact that software development has become a global business. However, as more and more CCSDT are formed, many challenges and issues have been raised in those cross-cultural environments stemming from miscommunication, misunderstanding, cultural differences and conflicts. This paper uses the SGM to help resolve the issues.
Joint torques during sit-to-stand in healthy subjects and people with Parkinson's disease.
(2003)

OBJECTIVES
To compare lower limb joint torques during sit-to-stand in normal elderly subjects and people with Parkinson's disease, using a developed biomechanical model simulating all phases of sit-to-stand.Design. A cross-sectional study utilizing a Parkinsonian and a control group.


BACKGROUND
Subjects with Parkinson's disease were observed to experience difficulty in performing sit-to-stand. The developed model was used to calculate the lower limb joint torques in normal elderly subjects and subjects with Parkinson's disease, to delineate possible causes underlying difficulties in initiating sit-to-stand task.


METHODS
Six normal elderly subjects and seven age-matched subjects with Parkinson's disease performed five sit-to-stand trials at their self-selected speed. Anthropometric data, two-dimensional kinematic and foot-ground and thigh-chair reactive forces were used to calculate, via inverse dynamics, the joint torques during sit-to-stand in both before and after seat-off phases. The difference between the control and Parkinson's disease group was analysed using independent t-tests.


RESULTS
Both control and Parkinson's disease groups had a similar joint kinematic pattern, although the Parkinson's disease group demonstrated a slower angular displacement. The latter subjects produced significantly smaller normalized hip flexion torque and presented a slower torque build-up rate than the able-bodied subjects (P<0.05).


CONCLUSION
Slowness of sit-to-stand in people with Parkinson's disease could be due to a reduced hip flexion joint torque and a prolonged rate of torque production.
Deep Convolutional Neural Networks for Fire Detection in Images
(2017)

Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balanced benchmark dataset provided in this paper. We therefore propose to use even deeper Convolutional Neural Networks for fire detection in images, and enhancing these with fine tuning based on a fully connected layer. We use two pretrained state-of-the-art Deep CNNs, VGG16 and Resnet50, to develop our fire detection system. The Deep CNNs are tested on our imbalanced dataset, which we have assembled to replicate real world scenarios. It includes images that are particularly difficult to classify and that are deliberately unbalanced by including significantly more non-fire images than fire images. The dataset has been made available online. Our results show that adding fully connected layers for fine tuning indeed does increase accuracy, however, this also increases training time. Overall, we found that our deeper CNNs give good performance on a more challenging dataset, with Resnet50 slightly outperforming VGG16. These results may thus lead to more successful fire detection systems in practice.
Multi-task low-rank affinity pursuit for image segmentation
(2011)

This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and ℓ2;1-norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods.
A 240 GHz ultra-wideband FMCW radar system with on-chip antennas for high resolution radar imaging
(2013)

Nowadays emerging industrial radar applications demand for high-resolution, high-precision and at the same time low-cost radar sensors. Recent advances in semiconductor technology allow highly integrated radar sensors at frequencies up to several hundred GHz in mass-production suitable and cost-effective SiGe bipolar technologies. In this contribution, a SiGe MMIC-based 240 GHz radar sensor with more than 60GHz bandwidth is presented. It consists of a MMIC chip including the high-frequency components and a digital control module with the PLL stabilisation, ramp generation, and data acquisition. The antenna is realized by on-chip patch antennas, which are focused by using an additional dielectric lens. The radar allows fast and highly linear frequency sweeps from 204 GHz to 265 GHz with an maximum output power of ≈ -1dBm EIRP (patch only). A phase noise of <;-65 dBc/Hz (>1 kHz offset) is achieved over the complete tuning range. Additionally range profile, jitter and imaging measurements are presented to demonstrate the achieved system performance.
Prefrontal neuropsychological effects of sleep deprivation in young adults--a model for healthy aging?
(2000)

Neuropsychological testing and brain imaging show that healthy aging leads to a preferential impairment of the prefrontal cortex (PFC). Interestingly, in young adults sleep deprivation (SD) has similar effects. Psychological tasks not so oriented to the PFC are less sensitive both to SD and aging. The PFC is a cortical region working particularly hard during wakefulness, which may make it more vulnerable to "deterioration," whether this is through aging or SD. In these respects SD in young adults may offer a model for aging. No study has directly compared aging with SD. We compared groups comprising (equal sexes): YOUNG (av. 23y), MIDDLE AGED (av. 60y) and OLD (av. 73y). Young were subdivided into SD and non-sleep deprived groups. All participants were carefully screened, were healthy, good sleepers and with a similar educational background. A battery of PFC-oriented, short and straightforward neuropsychological tests was used to compare the effects of 36h of SD in the young group, with findings from the healthy, alert, non-sleep deprived groups. Tests relied on accuracy rather than speed (which took into account the problem of "global slowing" in the older participants), and were administered once (i.e., were novel). Test outcomes were significantly affected by: (1) SD in the young groups, and (2) by age. A non-PFC component of one test was not affected by SD or by age. It was concluded that 36h SD in young adults produces effects on the PFC similar to those found in normal, alert people aged about 60 years. However, it can not be concluded that an aged brain is a sleep-deprived brain.
A new approach for sparse Bayesian channel estimation in SCMA uplink systems
(2016)

The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.
Using BPM governance to align systems and practice
(2011)

Purpose – The purpose of this paper is to propose a business process management (BPM) governance model that sets BPM decision making, along with roles and responsibilities. The setting context of the study is a government-owned corporation operating in Australia. Design/methodology/approach – A qualitative case study examined and analysed organisational documents using a content analysis approach. Results of document analysis are used to inform a series of in-depth interviews of key stakeholders in the organisation. Interviews are analysed using a constant comparison method to derive themes and build categories of description. Findings – A BPM governance model is proposed. Results of thematic analysis are interpreted against the framework of the BPM governance model, leading to findings that include implications for theory and practice. Practical implications – In practical terms, the research shows how BPM practice can be aligned and integrated with the corporate governance and management systems in the selected case study organisation. Originality/value – Despite research identifying the importance of governance, along with associated capabilities, there has been little progress on how the abovementioned capabilities can be effectively deployed across an organisation. This paper addresses a gap in the literature relating to how to deploy BPM governance in an organisation.
Inferring Strategies for Sentence Ordering in Multidocument News Summarization
(2002)

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.
A Maximum-Entropy-Inspired Parser
(2000)

We present a new parser for parsing down to Penn tree-bank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trMned and tested on the previously established [5,9,10,15,17] "standard" sections of the Wall Street Journal treebank. This represents a 13% decrease in error rate over the best single-parser results on this corpus [9]. The major technical innovation is tire use of a "ma~ximum-entropy-inspired" model for conditioning and smoothing that let us successfully to test and combine many different conditioning events. We also present some partial results showing the effects of different conditioning information, including a surprising 2% improvement due to guessing the lexical head's pre-terminal before guessing the lexical head.
Estimation of probabilities from sparse data for the language model component of a speech recognizer
(1987)

The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. While the method has been developed for and successfully implemented in the IBM Real Time Speech Recognizers, its generality makes it applicable in other areas where the problem of estimating probabilities from sparse data arises. Sparseness of data is an inherent property of any real text, and it is a problem that one always encounters while collecting frequency statistics on words and word sequences (m-grams) from a text of finite size. This means that even for a very large data collection, the maximum likelihood estimation method does not allow us to adequately estimate probabilities of rare but nevertheless possible word sequences-many sequences occur only once (“singletons”); many more do not occur at all. Inadequacy of the maximum likelihood estimator and the necessity to estimate the probabilities of m-grams which did not occur in the text constitute the essence of the problem. The main idea of the proposed solution to the problem is to reduce unreliable probability estimates given by the observed frequencies and redistribute the “freed” probability “mass” among m-grams which never occurred in the text. The reduction is achieved by replacing maximum likelihood estimates for m-grams having low counts with renormalized Turing’s estimates [l], and the redistribution is done via the recursive utilization of lower level conditional distributions. We found Turing’s method attractive because of its simplicity and its characterization as the optimal empirical Bayes’ estimator of a multinomial probability. Robbins in [2] introduces the empirical Bayes’ methodology and Nadas in [3] gives various derivations of the Turing’s formula. Let N be a sample text size and let n, be the number of words (m-grams) which occurred in the text exactly r times, so that N = C rn,. (1) Turing’s estimate PT for a probability of a word (m-gram) which occurred in the sample r times is r